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Original Contribution |

Impact of Hospital Volume on Operative Mortality for Major Cancer Surgery FREE

Colin B. Begg, PhD; Laura D. Cramer, ScM; William J. Hoskins, MD; Murray F. Brennan, MD
[+] Author Affiliations

From the Departments of Epidemiology and Biostatistics (Dr Begg and Ms Cramer) and Surgery (Drs Hoskins and Brennan), Memorial Sloan-Kettering Cancer Center, New York, NY.


JAMA. 1998;280(20):1747-1751. doi:10.1001/jama.280.20.1747.
Text Size: A A A
Published online

Context.— Hospitals that treat a relatively high volume of patients for selected surgical oncology procedures report lower surgical in-hospital mortality rates than hospitals with a low volume of the procedures, but the reports do not take into account length of stay or adjust for case mix.

Objective.— To determine whether hospital volume was inversely associated with 30-day operative mortality, after adjusting for case mix.

Design and Setting.— Retrospective cohort study using the Surveillance, Epidemiology, and End Results (SEER)–Medicare linked database in which the hypothesis was prospectively specified. Surgeons determined in advance the surgical oncology procedures for which the experience of treating a larger volume of patients was most likely to lead to the knowledge or technical expertise that might offset surgical fatalities.

Patients.— All 5013 patients in the SEER registry aged 65 years or older at cancer diagnosis who underwent pancreatectomy, esophagectomy, pneumonectomy, liver resection, or pelvic exenteration, using incident cancers of the pancreas, esophagus, lung, colon, and rectum, and various genitourinary cancers diagnosed between 1984 and 1993.

Main Outcome Measure.— Thirty-day mortality in relation to procedure volume, adjusted for comorbidity, patient age, and cancer stage.

Results.— Higher volume was linked with lower mortality for pancreatectomy (P=.004), esophagectomy (P<.001), liver resection (P=.04), and pelvic exenteration (P=.04), but not for pneumonectomy (P=.32). The most striking results were for esophagectomy, for which the operative mortality rose to 17.3% in low-volume hospitals, compared with 3.4% in high-volume hospitals, and for pancreatectomy, for which the corresponding rates were 12.9% vs 5.8%. Adjustments for case mix and other patient factors did not change the finding that low volume was strongly associated with excess mortality.

Conclusions.— These data support the hypothesis that when complex surgical oncologic procedures are provided by surgical teams in hospitals with specialty expertise, mortality rates are lower.

Figures in this Article

A NUMBER of cancer studies have been conducted using hospital volume of patients treated as a measure of surgical expertise, following a tradition of the use of patient volume in studies of variations in outcomes between hospitals and between surgeons.1 In the United States, all population-based studies of this issue have used state discharge databases and in-hospital mortality as the end point, notably the studies of pancreatectomy in New York,2 California,3 and Maryland4; studies of lung cancer in California5; and hepatic resections in Maryland.6 All these studies demonstrated much lower mortality in high-volume hospitals.

There are significant limitations to the use of discharge data for this purpose. First, one must use in-hospital mortality as the end point, but this could be affected by hospital policies regarding length of stay. Case mix adjustments for disease severity are limited by the availability and quality of data on disease severity in the discharge database. Finally, one cannot effectively identify individual patients and link them to their cancer diagnosis for the purpose of creating a population-based cohort of incident cases and for determining important factors such as time since diagnosis.

In our study, we circumvented these problems by accessing the Surveillance, Epidemiology, and End Results (SEER)–Medicare linked database.7 Use of the SEER database permitted the creation of a population-based census of incident cancer patients during the target time period for the study (1984-1993). Linkage to Medicare permitted, for patients older than 65 years, identification of precise details of the surgical procedures performed, if any, including dates, information on comorbidities, and follow-up data on survival. The most critical attribute of this approach is the ability to determine survival at a landmark time point, 30 days after surgery, thereby eliminating the need to use the potentially biased discharge status in evaluating mortality rates.

To our knowledge, the only study that has evaluated postoperative 30-day mortality in a similar population-based fashion is the study of colorectal cancer surgery in Scotland,8 in which variations in mortality rates among surgeons were observed, although there was no apparent effect of surgeon volume.

Selection of Study Hypotheses

In studying a general hypothesis using a large database with information on all cancers, there is a danger of overinterpreting apparent correlations due to the many sites and procedures that could be studied and the various ways that mortality and volume could be defined. Thus, we approached this study in a hypothesis-driven fashion by developing a protocol and specifying in advance the precise details of our methodology, including the cancer sites and procedures to be studied, the measures of volume and mortality to be used, and the statistical tests to be used. This protocol was reviewed and approved by representatives from the National Cancer Institute (SEER) and the Health Care Financing Administration (Medicare) primarily to address issues of confidentiality and feasibility. After this approval, we were given access only to data for the sites of disease specified in our protocol. The analysis and interpretation of the data are the sole responsibility of the authors and do not represent the views of either the National Cancer Institute or Health Care Financing Administration.

Specific Procedures Studied and Rationale

Experienced surgeons at our institution determined on the basis of collective knowledge the procedures that they believed to be sufficiently complex that mortality differences should be detectable between high-volume and low-volume hospitals. Five procedures were selected: pancreatectomy, including proximal pancreatectomy (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 52.51), radical subtotal pancreatectomy (ICD-9-CM 52.53), other partial pancreatectomy (ICD-9-CM 52.59), total pancreatectomy (ICD-9-CM 52.6), and radical pancreaticoduodenectomy (ICD-9-CM 52.7); esophagectomy, including esophagectomy not otherwise specified (ICD-9-CM 42.40), partial esophagectomy (ICD-9-CM 42.41), and total esophagectomy (ICD-9-CM 42.42); complete pneumonectomy (ICD-9-CM 32.5); hepatic resection, including partial hepatectomy (ICD-9-CM 50.22), and lobectomy of liver (ICD-9-CM 50.3); pelvic exenteration, including radical cystectomy (ICD-9-CM 57.71); and pelvic evisceration (ICD-9-CM 68.8). In general, these procedures involve preoperative judgment, diagnostic accuracy, meticulous surgical technique, and demanding postoperative care. Thus, they are all major procedures with significant risk of serious postoperative morbidity and mortality.

SEER-Medicare Linked Database

The SEER database consists of all incident cases of cancer in several defined geographic populations comprising approximately 10% of the population of the United States.9 The Medicare database has information on all Medicare claims, and it encompasses 97% of individuals aged 65 years or older. The Medicare Provider Analysis and Review files contain records on 100% of hospital admissions since 1984.7 Up to 5 diagnoses and up to 3 procedures were coded using the ICD-9-CM between 1984 and 1991, with a subsequent increase in the number of codes available. This file also contains dates of the procedures and the date of death. Investigators at the National Cancer Institute and the Health Care Financing Administration have succeeded in matching 94% of the SEER cases with their Medicare records.7

Data

Potential study subjects comprised incident cases of the cancers that made them candidates for 1 of the 5 following procedures: pancreatectomy, cancer of the pancreas (ICD-9-CM 157); esophagectomy, cancer of the esophagus (ICD-9-CM 150); pneumonectomy, cancer of the lung or bronchus (ICD-9-CM 162.2-9); hepatic resection, liver metastases following cancers of the colon and rectum (ICD-9-CM 153, 154.0, 154.1, 154.8); or pelvic exenteration for cancers of the cervix, endometrium, bladder, colon, and rectum (ICD-9-CM 153, 154, 173.5, 179, 180, 182, 184.0-2, 184.4-9, 188, 195.3). The study included all cases incident in the SEER registry between 1984 and 1993, inclusive. Our protocol specified that the patient be included in the analysis only if the designated procedure was performed within 2 months of diagnosis, to limit patient heterogeneity, except for hepatic resection, for which the number of procedures at the time of incidence was inadequate for meaningful analysis. Mortality was defined as death within 30 days of hospitalization. We used the date of hospitalization as a replacement for the date of surgery, since the former is coded more reliably in the Medicare database.

Validity of Volume Measure

Hospitals were classified by volume on the basis of the total number of procedures performed in this study between 1984 and 1993. Since the study was restricted to patients 65 years or older, we were concerned that our measure of volume might not accurately reflect experience with the procedure because many procedures would be performed in the hospitals in younger patients and in patients who were not diagnosed as having cancer. To evaluate the validity of our measure of volume, we examined data from the New York State discharge database. We identified all hospitals at which the index procedure had been performed at least 6 times between the years 1990 and 1995 in cancer patients 65 years or older. We ranked these hospitals according to the number of procedures performed in patients 65 years or older and also according to the total number of procedures across all ages. The rank correlations of these 2 rankings are presented in Table 1, using the Kendall rank statistic.10 The high values of these correlations support the use of volume as calculated in our study as a valid measure of the ranking of hospitals based on the total volume of procedures conducted.

Table Graphic Jump LocationTable 1.—Validation Study Using New York State Discharge Database*
Statistical Analysis and Power

Our protocol specified that we would assess the impact of volume on mortality using the Mantel-Haenszel test for trend.11 We elected to perform this analysis without any aggregation to maximize power and eliminate the opportunity to select cut points to optimize the P values. However, in our graphs we display mortality rates in 3 aggregated volume groupings for visual effect. Confidence intervals (CIs) are 95% exact intervals for binomial proportions. Our protocol did not specify precisely how we would accomplish adjustments for case mix. After evaluating the availability and quality of the data, we elected to use a modified comorbidity index. Our index is the one proposed by Romano et al,12 with the contributions "any malignancy" and "metastatic solid tumor" eliminated. A validation study of this index was conducted by Ghali et al.13 The index is a modification of the one originally proposed by Charlson et al14 and it comprises a weighted sum of designated comorbid illnesses. We also adjusted for patient age, grouped into 3 categories (ages 65-69, 70-74, and ≥75 years). In addition, our linkage to the SEER database allowed us to evaluate the influence of the extent of disease by adjusting for cancer stage. The impact of volume on mortality was adjusted for these factors using logistic regression in which the outcome of the patient was that he/she was alive or dead at 30 days, volume was entered as a continuous variable, and comorbidity, cancer stage, and age were classified using indicator variables for the categories in Table 4, Table 5, and Table 6. The power of the study was projected in the protocol based on results of earlier studies of pancreatectomy and lung cancer in the literature and a 2-sided test at the 5% significance level. These projections indicated high power, in excess of 95%. Power was not estimated for the remaining procedures due to lack of available data to make meaningful projections.

Accrual of incident cases into the study is described in Table 2. The data show that a relatively small percentage of elderly patients with these cancers are candidates for these highly invasive procedures. For example, among the 19,205 incident cases of cancer of the pancreas in SEER, diagnosed at age 65 years or older between 1984 and 1993, only 742 patients (3.9%) underwent a pancreatectomy within 2 months of having been diagnosed as having cancer. These percentages are somewhat higher but still relatively low when we restrict attention to patients with localized or regional disease.

Table Graphic Jump LocationTable 2.—Patient Selection Statistics

The 30-day mortality rates are presented by volume in Table 3. Thus, for pancreatectomy, the first entry presents combined results for the 126 hospitals, each of which performed 1 pancreatectomy, resulting in an average 30-day mortality rate of 14% (18/126). Statistical analysis for a trend of decreasing mortality with increasing volume leads to P=.004 for pancreatectomy, P<.001 for esophagectomy, P=.32 for pneumonectomy, P=.04 for hepatic resection, and P=.04 for pelvic exenteration. The precise nature of the trends are observed more clearly in the Figure 1, in which the volume categories have been aggregated.

Table Graphic Jump LocationTable 3.—Mortality Rates by Hospital Volume
Graphic Jump Location
The points on the graphs represent 30-day mortality rates, aggregated across the designated volume categories. Error bars represent 95% confidence intervals.

The potential impact of case mix on these results is displayed in Table 4, Table 5, and Table 6. In Table 4, the aggregated volume categories are cross-tabulated with the comorbidity index. In general, the distribution of comorbidity is not strongly related to volume, although this association is significant for pancreatectomy (P=.04). No evidence of appreciable patient selectivity on the basis of cancer stage (Table 5) or patient age (Table 6) exists, with the exception that low-volume institutions more frequently report the patient to SEER as unstaged. The relatively small percentages of cases with metastatic disease were presumably restaged as such because of distant spread discovered during the procedure. The P values for the effects of volume on mortality for each site, after adjusting for these factors using logistic regression, were as follows: pancreatectomy P=.01; esophagectomy P<.001; pneumonectomy P=.19; hepatic resection P=.05; pelvic exenteration P=.05. Thus, the results are essentially unaffected by the case mix adjustments. Finally, there was no evidence that discharge prior to death within 30 days had any influence on the results. This was a relatively rare occurrence, except for pneumonectomy, which was evenly distributed across volume categories.

The data provide strong evidence that experience in performing these complex procedures, as represented by hospital volume, results in substantially lower operative mortality. The results are particularly striking for esophagectomy, for which the 30-day mortality drops from 17.3% (95% CI, 13.3%22.0%) in the lowest volume category to 3.4% (95% CI, 0.7%-9.6%) in the highest volume category. For the other sites the corresponding reductions were as follows: pancreatectomy, 12.9% (95% CI, 9.7%-16.6%) to 5.8% (95% CI, 2.5%-11.0%); pneumonectomy, 13.8% (95% CI, 10.9%-17.2%) to 10.7% (95% CI, 8.0%-14.0%); hepatic resection, 5.4% (95% CI, 3.6%-7.8%) to 1.7% (95% CI, 0.4%-5.0%); and pelvic exenteration, 3.7% (95% CI, 2.3%-5.5%) to 1.5% (95% CI, 0.7%-2.8%).

Clinically, these differences are understandable, since esophagectomy and pancreatectomy are procedures for which morbidity is high, and serious morbidity, such as fistula formation, commonly translates into mortality. In pneumonectomy and hepatectomy, the outcome is highly influenced by the intraoperative decision to proceed in a low-risk patient. This is highlighted by the fact that for hepatic resections the preponderance of procedures are less than lobar (69%). Removal of the pelvic viscera (pelvic exenteration) requires removal of the genital organs in combination with the bladder or rectum or both, followed by reconstruction and a diversion of the urinary and gastrointestinal tract. Most centers that perform these procedures in volume develop a 2-team approach in which one team performs the exenteration and the second team performs the reconstruction. Thus, effective coordination of the surgical staff members is important.

Our study demonstrates that these procedures are performed on relatively small proportions of patients with incident cancers, at least in the restricted age group under investigation. For example, our study indicates that only 3.9% of incident cases of pancreatic cancer undergo pancreatic resection within 2 months of diagnosis. Thus, the patients evaluated in our study constitute a highly selected group, and one can only speculate at the selection factors that play a role. Despite this, our evaluation of comorbidity appears to indicate little evidence that the high-volume hospitals are operating on a more favorable group of patients. The distributions of the comorbidity index as well as cancer stage and age are largely independent of hospital volume, and as a result, the statistical tests are unaffected by adjustments for these factors.

The representativeness of the SEER population and its providers for health care research of this nature is a topic that has received recent attention. Nattinger et al15 have compared the SEER catchment to the entire United States with respect to a variety of characteristics. Although some significant differences were observed, there was no significant difference in either the density of board-certified oncologists or of general surgical specialists. The most cogent issue regarding generalizability in our study is that the use of Medicare restricted our population to patients aged 65 years or older. It is likely that the overall surgical mortality rate should be lower in younger age groups. This is indicated, for example, for pancreatic cancer in 1 of the discharge database studies.2 Despite the possible influence of age on mortality, it is certainly plausible, indeed likely, that the trend of lower mortality with increased volume may be unaffected by the age restriction in our study.

Our results are broadly consistent with the previous population-based studies of these procedures cited earlier,26 although a multi-institutional study of pancreatectomy showed no volume effect16 and the large survey of pancreatic cancer conducted by the American College of Surgeons detected only a small volume trend.17 Variations between surgeons within individual hospitals favoring experienced surgeons have also been observed for pancreatectomy in studies by Pellegrini et al18 and Andren-Sandberg and Ihse.19 Related research in studies of breast cancer in the United Kingdom showed specialty care to be associated with long-term survival in 2 studies20,21 and with quality-of-life outcomes in another.22 Finally, a large study of ovarian cancer demonstrated inferior long-term survival in patients treated by general surgeons compared with specialists.23

In summary, our study contributes to the growing literature supporting the hypothesis that specialist cancer care significantly improves patient outcomes, with the caveat that we are using patient volume to represent specialization. When procedures such as pancreatectomy and esophagectomy are attempted, there is strong evidence that these can be performed more safely in high-volume referral centers. The data support a similar conclusion for hepatic resection and pelvic exenteration, although with less statistical conviction.

Hughes RG, Hunt SS, Luft HS. Effects of surgeon volume and hospital volume on quality of care in hospitals.  Med Care.1987;25:489-503.
Lieberman MD, Kilburn H, Lindsey M, Brennan MF. Relation of perioperative deaths to hospital volume among patients undergoing pancreatic resection for malignancy.  Ann Surg.1995;222:638-645.
Glasgow RE, Mulvihill SJ. Hospital volume influences outcome in patients undergoing pancreatic resection for cancer.  West J Med.1996;165:294-300.
Gordon TA, Burleyson GP, Tielsch JM, Cameron JL. The effects of regionalization on cost and outcome for one general high-risk surgical procedure.  Ann Surg.1995;221:43-49.
Romano PS, Mark DH. Patient and hospital characteristics related to in-hospital mortality after lung cancer resection.  Chest.1992;101:1332-1337.
Choti MA, Bowman NM, Pitt HA.  et al.  Should hepatic resections be performed at high volume referral centers?  J Gastrointest Surg.1998;2:11-20.
Potosky AL, Riley GF, Lubitz JD, Mentnech RM, Kessler LG. Potential for cancer related health services research using a linked Medicare-tumor registry database.  Med Care.1993;31:732-748.
McArdle CS, Hole D. Impact of variability among surgeons on postoperative morbidity and mortality and ultimate survival.  BMJ.1991;302:1501-1505.
Kosary CL, Ries LAG, Miller BA, Hankey BF, Harras A, Edwards BK. SEER Cancer Statistics Review, 1973-1992: Tables and Graphs. Bethesda, Md: National Cancer Institute, National Institutes of Health: NIH publication 96-2789.
Armitage P, Berry G. Statistical Methods in Medical ResearchOxford, England: Blackwell Scientific Publications; 1994:464-467.
Mantel N. Chi-square tests with one degree of freedom: extensions of the Mantel-Haenszel procedure.  J Am Stat Assoc.1963;58:690-700.
Romano PS, Roos LL, Jollis JG. Adapting a clinical co-morbidity index for use with ICD-9-CM administrative data: differing perspectives.  J Clin Epidemiol.1993;46:1075-1079.
Ghali WA, Hall RE, Rosen AK, Ash AS, Moskowitz MA. Searching for an improved clinical comorbidity index for use with ICD-9-CM administrative data.  J Clin Epidemiol.1996;49:273-278.
Charlson ME, Pompei P, Ales KL, Mackenzie CR. A new method of classifying prognostic co-morbidity in longitudinal studies: development and validation.  J Chronic Dis.1987;40:373-383.
Nattinger AB, McAuliffe TL, Schapira MM. Generalizability of the Surveillance, Epidemiology and End Results registry population: factors relevant to epidemiologic and health care research.  J Clin Epidemiol.1997;50:939-945.
Edge SB, Schmieg Jr RE, Rosenlof LK, Wilhelm MC. Pancreas cancer resection outcome in American university centers in 1989-1990.  Cancer.1993;71:3502-3508.
Janes RH, Niederhuber JE, Chmiel JS.  et al.  National patterns of care for pancreatic cancer: results of a survey by the Commission on Cancer.  Ann Surg.1996;223:261-272.
Pellegrini CA, Heck CF, Raper S, Way LW. An analysis of the reduced morbidity and mortality rates after pancreaticoduodenectomy.  Arch Surg.1989;124:778-781.
Andren-Sandberg A, Ihse I. Factors influencing survival after total pancreatectomy in patients with pancreatic cancer.  Ann Surg.1983;198:605-610.
Sainsbury R, Haward B, Rider L, Johnston C, Round C. Influence of clinician workload and patterns of treatment on survival from breast cancer.  Lancet.1995;345:1265-1270.
Gillis CR, Hole DJ. Survival outcome of care by specialist surgeons in breast cancer: a study of 3786 patients in the west of Scotland.  BMJ.1996;312:145-153.
Ma M, Bell J, Campbell S, Basnett I, Pollock A, Taylor I. Breast cancer management: is volume related to quality?  Br J Cancer.1997;75:1652-1659.
Nguyen HN, Averette HE, Hoskins W, Penalver M, Sevin BU, Steren A. National Survey of Ovarian Carcinoma Part V: the impact of physician's specialty on patients' survival.  Cancer.1993;72:3663-3670.

Figures

Graphic Jump Location
The points on the graphs represent 30-day mortality rates, aggregated across the designated volume categories. Error bars represent 95% confidence intervals.

Tables

Table Graphic Jump LocationTable 1.—Validation Study Using New York State Discharge Database*
Table Graphic Jump LocationTable 2.—Patient Selection Statistics
Table Graphic Jump LocationTable 3.—Mortality Rates by Hospital Volume

References

Hughes RG, Hunt SS, Luft HS. Effects of surgeon volume and hospital volume on quality of care in hospitals.  Med Care.1987;25:489-503.
Lieberman MD, Kilburn H, Lindsey M, Brennan MF. Relation of perioperative deaths to hospital volume among patients undergoing pancreatic resection for malignancy.  Ann Surg.1995;222:638-645.
Glasgow RE, Mulvihill SJ. Hospital volume influences outcome in patients undergoing pancreatic resection for cancer.  West J Med.1996;165:294-300.
Gordon TA, Burleyson GP, Tielsch JM, Cameron JL. The effects of regionalization on cost and outcome for one general high-risk surgical procedure.  Ann Surg.1995;221:43-49.
Romano PS, Mark DH. Patient and hospital characteristics related to in-hospital mortality after lung cancer resection.  Chest.1992;101:1332-1337.
Choti MA, Bowman NM, Pitt HA.  et al.  Should hepatic resections be performed at high volume referral centers?  J Gastrointest Surg.1998;2:11-20.
Potosky AL, Riley GF, Lubitz JD, Mentnech RM, Kessler LG. Potential for cancer related health services research using a linked Medicare-tumor registry database.  Med Care.1993;31:732-748.
McArdle CS, Hole D. Impact of variability among surgeons on postoperative morbidity and mortality and ultimate survival.  BMJ.1991;302:1501-1505.
Kosary CL, Ries LAG, Miller BA, Hankey BF, Harras A, Edwards BK. SEER Cancer Statistics Review, 1973-1992: Tables and Graphs. Bethesda, Md: National Cancer Institute, National Institutes of Health: NIH publication 96-2789.
Armitage P, Berry G. Statistical Methods in Medical ResearchOxford, England: Blackwell Scientific Publications; 1994:464-467.
Mantel N. Chi-square tests with one degree of freedom: extensions of the Mantel-Haenszel procedure.  J Am Stat Assoc.1963;58:690-700.
Romano PS, Roos LL, Jollis JG. Adapting a clinical co-morbidity index for use with ICD-9-CM administrative data: differing perspectives.  J Clin Epidemiol.1993;46:1075-1079.
Ghali WA, Hall RE, Rosen AK, Ash AS, Moskowitz MA. Searching for an improved clinical comorbidity index for use with ICD-9-CM administrative data.  J Clin Epidemiol.1996;49:273-278.
Charlson ME, Pompei P, Ales KL, Mackenzie CR. A new method of classifying prognostic co-morbidity in longitudinal studies: development and validation.  J Chronic Dis.1987;40:373-383.
Nattinger AB, McAuliffe TL, Schapira MM. Generalizability of the Surveillance, Epidemiology and End Results registry population: factors relevant to epidemiologic and health care research.  J Clin Epidemiol.1997;50:939-945.
Edge SB, Schmieg Jr RE, Rosenlof LK, Wilhelm MC. Pancreas cancer resection outcome in American university centers in 1989-1990.  Cancer.1993;71:3502-3508.
Janes RH, Niederhuber JE, Chmiel JS.  et al.  National patterns of care for pancreatic cancer: results of a survey by the Commission on Cancer.  Ann Surg.1996;223:261-272.
Pellegrini CA, Heck CF, Raper S, Way LW. An analysis of the reduced morbidity and mortality rates after pancreaticoduodenectomy.  Arch Surg.1989;124:778-781.
Andren-Sandberg A, Ihse I. Factors influencing survival after total pancreatectomy in patients with pancreatic cancer.  Ann Surg.1983;198:605-610.
Sainsbury R, Haward B, Rider L, Johnston C, Round C. Influence of clinician workload and patterns of treatment on survival from breast cancer.  Lancet.1995;345:1265-1270.
Gillis CR, Hole DJ. Survival outcome of care by specialist surgeons in breast cancer: a study of 3786 patients in the west of Scotland.  BMJ.1996;312:145-153.
Ma M, Bell J, Campbell S, Basnett I, Pollock A, Taylor I. Breast cancer management: is volume related to quality?  Br J Cancer.1997;75:1652-1659.
Nguyen HN, Averette HE, Hoskins W, Penalver M, Sevin BU, Steren A. National Survey of Ovarian Carcinoma Part V: the impact of physician's specialty on patients' survival.  Cancer.1993;72:3663-3670.
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